Based on the successful 2014 book published by Apress, this textbook edition is expanded to provide a comprehensive history and state-of-the-art survey for fundamental computer vision methods. With over 800 essential references, as well as chapter-by-chapter learning assignments, both students and researchers can dig deeper into core computer vision topics. The survey covers everything from feature descriptors, regional and global feature metrics, feature learning architectures, deep learning, neuroscience of vision, neuralnet
works, and detailed example architectures to illustrate computer vision hardware and software optimization methods.
To complement the survey, the textbook includes useful analyses which provide insight into the goals of various methods, why they work, and how they may be optimized.
The text delivers an essential survey and a valuable taxonomy, thus providing a key learning tool for students, researchers and engineers, to supplement the many effective hands-on resources and open source projects, such as OpenCVand other imaging and deep learning tools.
Table of Contents
Chapter 1: Image Capture and Representation
Chapter 2: Image Pre-Processing
Chapter 3: Global and Regional Features
Chapter 4: Local Feature Design Concepts
Chapter 5: Taxonomy of Feature Description Attributes
Chapter 6: Interest Point Detector and Feature Descriptor Survey
Chapter 7: Ground Truth Data, Content, Metrics, and Analysis
Chapter 8: Vision Pipelines and Optimizations
Chapter 9: Feature Learning Architecture Taxonomy and Neuroscience Background
Chapter 10: Feature Learning and Deep Learning Architecture Survey
Appendix A: Synthetic Feature Analysis
Appendix B: Survey of Ground Truth Datasets
Appendix C: Imaging and Computer Vision Resources
Appendix D: Extended SDM Metrics
Appendix E: The Visual Genome Model (VGM)